Quantifying the contribution of airborne gravity data for geoid modeling: two case studies

2020 ◽  
Author(s):  
Tao Jiang ◽  
Yamin Dang ◽  
Chuanyin Zhang
Keyword(s):  
Large Scale ◽  
Gravity Data ◽  
Airborne Gravity ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Satellite Gravity ◽  
Terrestrial Gravity ◽  
Modeling Experiment ◽  
The Usa ◽  
Gps Leveling

<p>Airborne gravimetry has become increasingly important for geoid modeling because of its capability of collecting large scale gravity data over difficult areas. In order to quantify the contribution of airborne gravity data for geoid determination, two regions with distinct topographical condition, a hilly desert area in Mu Us of China and a mountainous region in Colorado of the USA were selected for gravimetric geoid modeling experiment. The gravimetric geoid model computed by combining satellite gravity model, terrestrial and airborne gravity data fits with GPS leveling data to 0.8 cm for Mu Us case and 5.3 cm for Colorado case. The contribution of airborne gravity data to the signal and accuracy improvement of the geoid was quantitatively evaluated for different spatial distribution and density of terrestrial gravity data. The results demonstrate that in the cases of the spacing of terrestrial gravity points exceeds 15 km, the additions of airborne gravity data improve the accuracies of gravimetric geoid models by 11.1%~48.3% for Mu Us case and 13%~20% for Colorado case.</p>

scholarly journals Gravimetric Geoid Modeling from the Combination of Satellite Gravity Model, Terrestrial and Airborne Gravity Data: A Case Study in the Mountainous Area, Colorado

2020 ◽  
Author(s):  
Tao Jiang ◽  
Yamin Dang ◽  
Chuanyin Zhang
Keyword(s):  
Standard Deviation ◽  
Gravity Model ◽  
Gravity Data ◽  
Combination Method ◽  
Airborne Gravity ◽  
Mountainous Area ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Satellite Gravity ◽  
Terrestrial Gravity

Abstract Constructing a high precision and high resolution gravimetric geoid model in the mountainous area is a quite challenging task because of the high, rough nature of topography and the geological complexity. One way out is to use as many gravity observations from different sources as possible such as satellite, terrestrial and airborne gravity data, thus the proper combination of heterogeneous gravity datasets is critical. In a rough topographic area in Colorado, we computed a set of gravimetric geoid models based on different combination modes of satellite gravity models, terrestrial and airborne gravity data using the spectral combination method. The gravimetric geoid model obtained from the combination of satellite gravity model GOCO06S and terrestrial gravity data agrees with the GPS leveling measured geoid heights at 194 benchmarks in 5.8 cm in terms of the standard deviation of discrepancies, and the standard deviation reduces to 5.3 cm after including the GRAV-D airborne gravity data collected at ~6.2 km altitude into the data combination. The contributions of airborne gravity data to the signal and accuracy improvements of the geoid models were quantified for different spatial distribution and density of terrestrial gravity data. The results demonstrate that, although the airborne gravity survey was flown at a high altitude, the additions of airborne gravity data improved the accuracies of geoid models by 13.4% - 19.8% in the mountainous area (elevations > 2000 m) and 12.7% - 21% (elevations < 2000 m) in the moderate area in the cases of terrestrial gravity data spacings are larger than 15 km.

scholarly journals Gravimetric geoid modeling from the combination of satellite gravity model, terrestrial and airborne gravity data: a case study in the mountainous area, Colorado

2020 ◽  
Author(s):  
Tao Jiang ◽  
Yamin Dang ◽  
Chuanyin Zhang
Keyword(s):  
Standard Deviation ◽  
Gravity Model ◽  
Gravity Data ◽  
Combination Method ◽  
Airborne Gravity ◽  
Mountainous Area ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Satellite Gravity ◽  
Terrestrial Gravity

Abstract Constructing a high precision and high resolution gravimetric geoid model in the mountainous area is a quite challenging task because of the the lack of terrestrial gravity observations, rough high, rough nature of topography and the geological complexity. One way out is to use as hight quality and well distributed satellite and airborne gravity data to fill the gravity data gapsmany gravity observations from different sources as possible such as satellite, terrestrial and airborne gravity data, thus the proper combination of heterogeneous gravity datasets is critical. In a rough topographic area in Colorado, we computed a set of gravimetric geoid models based on different combination modes of satellite gravity models, terrestrial and airborne gravity data using the spectral combination method. The gravimetric geoid model obtained from the combination of satellite gravity model GOCO06S and terrestrial gravity data agrees with the GPS leveling measured geoid heights at 194 benchmarks in 5.8 cm in terms of the standard deviation of discrepancies, and the standard deviation reduces to 5.3 cm after including the GRAV-D airborne gravity data collected at ~6.2 km altitude into the data combination. The contributions of airborne gravity data to the signal and accuracy improvements of the geoid models were quantified for different spatial distribution and density of terrestrial gravity data. The results demonstrate that, although the airborne gravity survey was flown at a high altitude, the additions of airborne gravity data improved the accuracies of geoid models by 13.4% - 19.8% in the mountainous area (elevations > 2000 m) and 12.7% - 21% (elevations < 2000 m) in the moderate area in the cases of terrestrial gravity data spacings are larger than 15 km.

scholarly journals Gravimetric geoid modeling from the combination of satellite gravity model, terrestrial and airborne gravity data: a case study in the mountainous area, Colorado

2020 ◽  
Author(s):  
Tao Jiang ◽  
Yamin Dang ◽  
Chuanyin Zhang
Keyword(s):  
Standard Deviation ◽  
Gravity Model ◽  
Gravity Data ◽  
Combination Method ◽  
Airborne Gravity ◽  
Mountainous Area ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Satellite Gravity ◽  
Terrestrial Gravity

Abstract Constructing a high precision and high resolution gravimetric geoid model in the mountainous area is a quite challenging task because of the lack of terrestrial gravity observations, rough topography and the geological complexity. One way out is to use high quality and well distributed satellite and airborne gravity data to fill the gravity data gaps, thus the proper combination of heterogeneous gravity datasets is critical. In a rough topographic area in Colorado, we computed a set of gravimetric geoid models based on different combination modes of satellite gravity models, terrestrial and airborne gravity data using the spectral combination method. The gravimetric geoid model obtained from the combination of satellite gravity model GOCO06S and terrestrial gravity data agrees with the GPS leveling measured geoid heights at 194 benchmarks in 5.8 cm in terms of the standard deviation of discrepancies, and the standard deviation reduces to 5.3 cm after including the GRAV-D airborne gravity data collected at ~6.2 km altitude into the data combination. The contributions of airborne gravity data to the signal and accuracy improvements of the geoid models were quantified for different spatial distribution and density of terrestrial gravity data. The results demonstrate that, although the airborne gravity survey was flown at a high altitude, the additions of airborne gravity data improved the accuracies of geoid models by 13.4% - 19.8% in the mountainous area (elevations > 2000 m) and 12.7% - 21% (elevations < 2000 m) in the moderate area in the cases of terrestrial gravity data spacings are larger than 15 km.

scholarly journals Gravimetric geoid modeling from the combination of satellite gravity model, terrestrial and airborne gravity data: a case study in the mountainous area, Colorado

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Tao Jiang ◽  
Yamin Dang ◽  
Chuanyin Zhang
Keyword(s):  
Standard Deviation ◽  
Gravity Model ◽  
Gravity Data ◽  
Combination Method ◽  
Airborne Gravity ◽  
Mountainous Area ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Satellite Gravity ◽  
Terrestrial Gravity

AbstractConstructing a high-precision and high-resolution gravimetric geoid model in the mountainous area is a quite challenging task because of the lack of terrestrial gravity observations, rough topography and the geological complexity. One way out is to use high-quality and well-distributed satellite and airborne gravity data to fill the gravity data gaps; thus, the proper combination of heterogeneous gravity datasets is critical. In a rough topographic area in Colorado, we computed a set of gravimetric geoid models based on different combination modes of satellite gravity models, terrestrial and airborne gravity data using the spectral combination method. The gravimetric geoid model obtained from the combination of satellite gravity model GOCO06S and terrestrial gravity data agrees with the GPS leveling measured geoid heights at 194 benchmarks in 5.8 cm in terms of the standard deviation of discrepancies, and the standard deviation reduces to 5.3 cm after including the GRAV-D airborne gravity data collected at ~ 6.2 km altitude into the data combination. The contributions of airborne gravity data to the signal and accuracy improvements of the geoid models were quantified for different spatial distribution and density of terrestrial gravity data. The results demonstrate that, although the airborne gravity survey was flown at a high altitude, the additions of airborne gravity data improved the accuracies of geoid models by 13.4%–19.8% in the mountainous area (elevations > 2000 m) and 12.7%–21% (elevations < 2000 m) in the moderate area in the cases of terrestrial gravity data spacings are larger than 15 km.

scholarly journals Contribution of GRAV-D airborne gravity to improvement of regional gravimetric geoid modelling in Colorado, USA

2021 ◽  
Vol 95 (5) ◽  
Author(s):  
Matej Varga ◽  
Martin Pitoňák ◽  
Pavel Novák ◽  
Tomislav Bašić
Keyword(s):  
Gravity Anomaly ◽  
Gravity Data ◽  
Airborne Gravity ◽  
Mountainous Area ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Terrestrial Gravity ◽  
The Third ◽  
Regional Gravimetric Geoid ◽  
Geoid Computation

AbstractThis paper studies the contribution of airborne gravity data to improvement of gravimetric geoid modelling across the mountainous area in Colorado, USA. First, airborne gravity data was processed, filtered, and downward-continued. Then, three gravity anomaly grids were prepared; the first grid only from the terrestrial gravity data, the second grid only from the downward-continued airborne gravity data, and the third grid from combined downward-continued airborne and terrestrial gravity data. Gravimetric geoid models with the three gravity anomaly grids were determined using the least-squares modification of Stokes’ formula with additive corrections (LSMSA) method. The absolute and relative accuracy of the computed gravimetric geoid models was estimated on GNSS/levelling points. Results exhibit the accuracy improved by 1.1 cm or 20% in terms of standard deviation when airborne and terrestrial gravity data was used for geoid computation, compared to the geoid model computed only from terrestrial gravity data. Finally, the spectral analysis of surface gravity anomaly grids and geoid models was performed, which provided insights into specific wavelength bands in which airborne gravity data contributed and improved the power spectrum.

scholarly journals A High-Resolution Gravimetric Geoid Model for Kuwait Using the Least-Squares Collocation

2022 ◽  
Vol 9 ◽  
Author(s):  
Hamad Al-Ajami ◽  
Ahmed Zaki ◽  
Mostafa Rabah ◽  
Mohamed El-Ashquer
Keyword(s):  
High Resolution ◽  
Least Squares ◽  
Geopotential Model ◽  
Gravimetric Geoid ◽  
Least Squares Collocation ◽  
Geoid Model ◽  
Terrestrial Gravity ◽  
Digital Elevation ◽  
Elevation Model ◽  
Gps Leveling

A new gravimetric geoid model, the KW-FLGM2021, is developed for Kuwait in this study. This new geoid model is driven by a combination of the XGM2019e-combined global geopotential model (GGM), terrestrial gravity, and the SRTM 3 global digital elevation model with a spatial resolution of three arc seconds. The KW-FLGM2021 has been computed by using the technique of Least Squares Collocation (LSC) with Remove-Compute-Restore (RCR) procedure. To evaluate the external accuracy of the KW-FLGM2021 gravimetric geoid model, GPS/leveling data were used. As a result of this evaluation, the residual of geoid heights obtained from the KW-FLGM2021 geoid model is 2.2 cm. The KW-FLGM2021 is possible to be recommended as the first accurate geoid model for Kuwait.

The experimental geoid 2020 – the first joint geoid model for the North American and Pacific Geopotential Datum 

2021 ◽  
Author(s):  
Yan Ming Wang ◽  
Xiaopeng Li ◽  
Kevin Ahlgren ◽  
Jordan Krcmaric ◽  
Ryan Hardy ◽  
...  
Keyword(s):  
North American ◽  
Gravity Data ◽  
The United States ◽  
Geodetic Survey ◽  
Airborne Gravity ◽  
Data Sets ◽  
National Geodetic Survey ◽  
Geoid Model ◽  
Satellite Gravity ◽  
The North

&lt;p&gt;For the upcoming North American-Pacific Geopotential Datum of 2022, the National Geodetic Survey (NGS), the Canadian Geodetic Survey (CGS) and the&amp;#160;National Institute of Statistics and Geography of Mexico (INEGI) computed the first joint experimental gravimetric geoid model (xGEOID) on 1&amp;#8217;x1&amp;#8217; grids that covers a region bordered by latitude 0 to 85 degree, longitude 180 to 350 degree east.&amp;#160;xGEOID20 models are computed using terrestrial gravity data, the latest satellite gravity model GOCO06S, altimetric gravity data DTU15, and an additional nine airborne gravity blocks of the GRAV-D project, for a total of 63 blocks. In addition, a digital elevation model in a 3&amp;#8221; grid was produced by combining MERIT, TanDEM-X, and USGS-NED and used for the topographic/gravimetric reductions. The geoid models computed from the height anomalies (NGS) and from the Helmert-Stokes scheme (CGS) were combined using two different weighting schemes, then evaluated against the independent GPS/leveling data sets. The models perform in a very similar way, and the geoid comparisons with the most accurate Geoid Slope Validation Surveys (GSVS) from 2011, 2014 and 2017 indicate that the&amp;#160;relative geoid accuracy&amp;#160;could be around 1-2 cm baseline lengths up to 300 km for these GSVS lines in the United States. The xGEOID20 A/B models were selected from the combined models based on the validation results. The geoid accuracies were also estimated using the forward modeling.&lt;/p&gt;

scholarly journals GRAVIMETRIC GEOID MODELLING OVER PENINSULAR MALAYSIA USING TWO DIFFERENT GRIDDING APPROACHES FOR COMBINING FREE AIR ANOMALY

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 515-522
Author(s):  
M. F. Pa’suya ◽  
A. H. M. Din ◽  
J. C. McCubbine ◽  
A. H. Omar ◽  
Z. M. Amin ◽  
...  
Keyword(s):  
Gravity Data ◽  
Reference Signal ◽  
Digital Terrain Model ◽  
Peninsular Malaysia ◽  
Airborne Gravity ◽  
Geopotential Model ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Free Air ◽  
Free Air Anomaly

Abstract. We investigate the use of the KTH Method to compute gravimetric geoid models of Malaysian Peninsular and the effect of two differing strategies to combine and interpolate terrestrial, marine DTU17 free air gravity anomaly data at regular grid nodes. Gravimetric geoid models were produced for both free air anomaly grids using the GOCE-only geopotential model GGM GO_CONS_GCF_2_SPW_R4 as the long wavelength reference signal and high-resolution TanDEM-X global digital terrain model. The geoid models were analyzed to assess how the different gridding strategies impact the gravimetric geoid over Malaysian Peninsular by comparing themto 172 GNSS-levelling derived geoid undulations. The RMSE of the two sets of gravimetric geoid model / GNSS-levelling residuals differed by approx. 26.2 mm. When a 4-parameter fit is used, the difference between the RMSE of the residuals reduced to 8 mm. The geoid models shown here do not include the latest airborne gravity data used in the computation of the official gravimetric geoid for the Malaysian Peninsular, for this reason they are not as precise.

scholarly journals The Uganda Gravimetric Geoid Model 2014 Computed by The KTH Method

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
L. E. Sjöberg ◽  
A. Gidudu ◽  
R. Ssengendo
Keyword(s):  
Gravity Field ◽  
Gravity Data ◽  
Geoid Determination ◽  
Geopotential Model ◽  
Gravimetric Geoid ◽  
Geoid Model ◽  
Satellite Mission ◽  
Terrestrial Gravity ◽  
Gravity Field Recovery ◽  
Goce Satellite

AbstractFor many developing countries such as Uganda, precise gravimetric geoid determination is hindered by the low quantity and quality of the terrestrial gravity data. With only one gravity data point per 65 km2, gravimetric geoid determination in Uganda appears an impossible task. However, recent advances in geoid modelling techniques coupled with the gravity-field anomalies from the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) satellite mission have opened new avenues for geoid determination especially for areas with sparse terrestrial gravity. The present study therefore investigates the computation of a gravimetric geoid model overUganda (UGG2014) using the Least Squares Modification of Stokes formula with additive corrections. UGG2014 was derived from sparse terrestrial gravity data from the International Gravimetric Bureau, the 3 arc second SRTM ver4.1 Digital Elevation Model from CGIAR-CSI and the GOCE-only global geopotential model GO_CONS_GCF_2_TIM_R5. To compensate for the missing gravity data in the target area, we used the surface gravity anomalies extracted from the World Gravity Map 2012. Using 10 Global Navigation Satellite System (GNSS)/levelling data points distributed over Uganda, the RMS fit of the gravimetric geoid model before and after a 4-parameter fit is 11 cm and 7 cm respectively. These results show that UGG2014 agrees considerably better with GNSS/levelling than any other recent regional/ global gravimetric geoid model. The results also emphasize the significant contribution of the GOCE satellite mission to the gravity field recovery, especially for areas with very limited terrestrial gravity data.With an RMS of 7 cm, UGG2014 is a significant step forward in the modelling of a “1-cm geoid” over Uganda despite the poor quality and quantity of the terrestrial gravity data used for its computation.

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